Do iron homeostasis biomarkers mediate the associations of liability to type 2 diabetes and glycemic traits in liver steatosis and cirrhosis: a two-step Mendelian randomization study

Background
 Previous studies, including Mendelian randomization (MR), have demonstrated type 2 diabetes (T2D) and glycemic traits are associated with increased risk of metabolic dysfunction-associated steatotic liver disease (MASLD). However, few studies have explored the underlying pathway, such as the role of iron homeostasis. Methods We used a two-step MR approach to investigate the associations of genetic liability to T2D, glycemic traits, iron biomarkers, and liver diseases. We analyzed summary statistics from various genome-wide association studies of T2D (n = 933,970), glycemic traits (n ≤ 209,605), iron biomarkers (n ≤ 246,139), MASLD (n ≤ 972,707), and related biomarkers (alanine aminotransferase (ALT) and proton density fat fraction (PDFF)). Our primary analysis was based on inverse-variance weighting, followed by several sensitivity analyses. We also conducted mediation analyses and explored the role of liver iron in post hoc analysis. Results Genetic liability to T2D and elevated fasting insulin (FI) likely increased risk of liver steatosis (ORliability to T2D: 1.14 per doubling in the prevalence, 95% CI: 1.10, 1.19; ORFI: 3.31 per log pmol/l, 95% CI: 1.92, 5.72) and related biomarkers. Liability to T2D also likely increased the risk of developing liver cirrhosis. Genetically elevated ferritin, serum iron, and liver iron were associated with higher risk of liver steatosis (ORferritin: 1.25 per SD, 95% CI 1.07, 1.46; ORliver iron: 1.15 per SD, 95% CI: 1.05, 1.26) and liver cirrhosis (ORserum iron: 1.31, 95% CI: 1.06, 1.63; ORliver iron: 1.34, 95% CI: 1.07, 1.68). Ferritin partially mediated the association between FI and liver steatosis (proportion mediated: 7%, 95% CI: 2–12%). Conclusions Our study provides credible evidence on the causal role of T2D and elevated insulin in liver steatosis and cirrhosis risk and indicates ferritin may play a mediating role in this association. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-024-03486-w.


Study design and data sources
Present key elements of the study design early in the article.Consider including a table listing sources of data for all phases of the study.For each data source contributing to the analysis, describe the following: a) Setting: Describe the study design and the underlying population, if possible.Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection, when available.
Methods -Study design, data sources; Supplementary Note-Data sources; Table S3 b) Participants: Give the eligibility criteria, and the sources and methods of selection of participants.Report the sample size, and whether any power or sample size calculations were carried out prior to the main analysis Methods -Study design, data sources; Supplementary Note-Data sources; Table S3

Data and data sharing
Provide the data used to perform all analyses or report where and how the data can be accessed, and reference these sources in the article.Provide the statistical code needed to reproduce the results in the article, or report whether the code is publicly accessible and if so, where All data used in this study can be found in the cited references and the URLs in the Acknowledgements and Supplementary Materials.

Data Sources T2D and glycemic traits
We obtained the genetic instruments of T2D of European descent from the Diabetes Meta-Analysis of Trans-Ethics associations studies (DIAMANTE) Consortium (80,154 cases and 853,816 controls) [1].T2D cases were defined as FG≥7mmol/l, or 2-hour glucose (2hGlu)≥11.1mmol/l,glycated hemoglobin (HbA1c)≥6.5%,on T2D treatment etc., while controls were defined as participants not fulfilling the case definition or not reporting as T2D [1].Genetic associations were estimated using a linear mixed model with adjustment for age, sex, and additional study-specific covariates [1].
Genetic associations were estimated from a linear mixed model with adjustment for age, sex, study-specific covariates, principal components and body mass index (BMI) (except for HbA1c) [2].The original GWAS showed the collider bias from adjusting for BMI had little impact on the associations of the signals (Table S3) [2].

Iron homeostasis biomarkers
We extracted the genetic instruments of iron homeostasis biomarkers including ferritin These adjusted values were then used to obtain genetic association with biomarkers.BOLT-LMM were taken into account population stratification and relatedness.Meta-analyses were used to combine the summary statistics from the three cohorts.(Table S3) [3].
imaging centre, scan date, scan time, and genotyping batch, and genetic relatedness derived from genotyped SNPs as a random effect [6].

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standard deviation (SD)) (n = 246,139), serum iron (SD, n = 163,511), TIBC (SD, n = 135,430), and TSAT (SD, n = 131,471) from GWAS summary statistics of participants of European ancestry from deCODE genetics project (Iceland), INTERVAL study (UK), and Danish Blood Donor Study (Denmark) [3].The iron biomarkers were inverse normal transformed (separately for each sex), and a generalized additive model was used to obtain age-adjusted biomarker values, and ditto for INTERVAL (additional adjusting for menopausal status, ABO blood group, BMI, smoking, alcohol, and iron supplementation).
cases), FinnGen (1,425 cases), and INTERMOUNTAIN (392 cases)[4].Steatosis cases were identified by the diagnostic codes of ICD-10 (K76.0)relating to Fatty (change of) liver, not elsewhere classified in electronic health records, while all-cause liver cirrhosis was defined by the diagnostic codes of ICD-10 (K70.2,K70.3, K70.4,K74.0, K74.1, K74.2, K74.6, K76.6 and KI85) related to cirrhosis and fibrosis[4].Genetic associations were estimated from logistic regressions with adjustment for age, sex and additionally adjusted for population stratification (relied on 40 principal components) in UKB, county of birth, blood sample availability for the individual and an indicator function for the overlap of the lifetime of the individual with the time span of the phenotype collection in deCODE, 10 principal components, FinnGen 1 or 2 chip or legacy genotyping batch in FinnGen, and first 10 principal components in INTERMOUNTAIN (Table

Assessment of assumptions Describe any methods or prior knowledge used to assess the assumptions or justify their validity Introduction, Figure 1B Sensitivity analyses and additional analyses
b) State whether the study protocol and details were pre-registered (as well as when and where) fetched the genetic instruments of steatosis (log odds, 9491 cases and 876,210 controls) from GWAS summary statistics of participants of European ancestry from UK Biobank (UKB, 5921 cases), Icelandic deCODE genetics study (deCODE, 785 cases), FinnGen (651 cases), and USA Intermountain dataset (INTERMOUNTAIN, 2,134 cases), and liver cirrhosis (log odds, 4809 cases and 967,898 controls) from UKB (2301 cases), deCODE (691